A user at times may be listening to a musical composition such as a song as performed by a particular artist, may be watching a multimedia presentation such as a movie by a particular artist, or the like, and thereafter may decide that he or she would like to enjoy a similar composition or presentation (hereinafter ‘work’), or would like to enjoy a work by a similar artist. In the prior art, such a user would have to rely on various commentary and/or recommendations by others, individual research, and the like to find such similar artists and/or works. Of course, such a method is imprecise and is bound to miss works/artists of interest as well as expose the user to works/artists that in fact are not satisfactorily similar to the user.
Algorithms do exist in the prior art that attempt to generate similar artists/works, such as for example the algorithm disclosed in U.S. Pat. No. 6,545,209, which is commonly assigned with the present disclosure and which is incorporated by reference in its entirety. However, such prior art as disclosed in U.S. Pat. No. 6,545,209 requires intensive human-generated data at an individual work level and therefore is relatively inefficient.
Accordingly, a need exists for a method and mechanism for determining similar artists/works that relies on data already generated for a plurality of artists and works and that therefore is relatively more efficient. Moreover, a need exists for such a method and mechanism that takes into account attributes and factors such as styles, tones, popularity, temporal factors, and the like.